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This study aims to predict the pollution level that threatens the MarilaoRiver, located in the province of Bulacan, Philippines. The inhabitants of thisarea are now being exposed to pollution. Contamination of this waterway comesfrom both formal and informal industries, such as a used lead-acid battery,open dumpsites metal refining, and other toxic metals. Using various waterquality parameters like Dissolved Oxygen (DO), Potential of Hydrogen (pH),Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) were the basisfor predicting the pollution level. This study used the Data Mining techniquebased on the sample data collected from January of 2013 to November of 2017.These were used as a training data and test results to predict the rivercondition with its corresponding pollution level classification indicated withthe used of colors such as Green for Normal, Yellow for Average, Orange forPolluted and Red for Highly Polluted. The model got an accuracy of 91.75% witha Kappa value of 0.8115, interpreted as Strong in terms of the level ofagreement.

Predicting Pollution Level Using Random Forest: A Case Study of Marilao River in Bulacan Province, Philippines